feat: implement BTC/ETH correlation features for improved model accuracy

- Added a new design document outlining the integration of BTC/ETH candle data as additional features in the XRP ML filter, enhancing prediction accuracy.
- Introduced `MultiSymbolStream` for combined WebSocket data retrieval of XRP, BTC, and ETH.
- Expanded feature set from 13 to 21 by including 8 new BTC/ETH-related features.
- Updated various scripts and modules to support the new feature set and data handling.
- Enhanced training and deployment scripts to accommodate the new dataset structure.

This commit lays the groundwork for improved model performance by leveraging the correlation between BTC and ETH with XRP.

tests/test_ml_features.py

@@ -4,6 +4,56 @@ import pytest
 from src.ml_features import build_features, FEATURE_COLS
 
 
+def _make_df(n=10, base_price=1.0):
+    """테스트용 더미 캔들 DataFrame 생성."""
+    closes = [base_price * (1 + i * 0.001) for i in range(n)]
+    return pd.DataFrame({
+        "close": closes, "high": [c * 1.01 for c in closes],
+        "low": [c * 0.99 for c in closes],
+        "volume": [1000.0] * n,
+        "rsi": [50.0] * n, "macd": [0.0] * n, "macd_signal": [0.0] * n,
+        "macd_hist": [0.0] * n, "bb_upper": [c * 1.02 for c in closes],
+        "bb_lower": [c * 0.98 for c in closes], "ema9": closes,
+        "ema21": closes, "ema50": closes, "atr": [0.01] * n,
+        "stoch_k": [50.0] * n, "stoch_d": [50.0] * n,
+        "vol_ma20": [1000.0] * n,
+    })
+
+
+def test_build_features_with_btc_eth_has_21_features():
+    xrp_df = _make_df(10, base_price=1.0)
+    btc_df = _make_df(10, base_price=50000.0)
+    eth_df = _make_df(10, base_price=3000.0)
+    features = build_features(xrp_df, "LONG", btc_df=btc_df, eth_df=eth_df)
+    assert len(features) == 21
+
+def test_build_features_without_btc_eth_has_13_features():
+    xrp_df = _make_df(10, base_price=1.0)
+    features = build_features(xrp_df, "LONG")
+    assert len(features) == 13
+
+def test_build_features_btc_ret_1_correct():
+    xrp_df = _make_df(10, base_price=1.0)
+    btc_df = _make_df(10, base_price=50000.0)
+    eth_df = _make_df(10, base_price=3000.0)
+    features = build_features(xrp_df, "LONG", btc_df=btc_df, eth_df=eth_df)
+    btc_closes = btc_df["close"]
+    expected_btc_ret_1 = (btc_closes.iloc[-1] - btc_closes.iloc[-2]) / btc_closes.iloc[-2]
+    assert abs(features["btc_ret_1"] - expected_btc_ret_1) < 1e-6
+
+def test_build_features_rs_zero_when_btc_ret_zero():
+    xrp_df = _make_df(10, base_price=1.0)
+    btc_df = _make_df(10, base_price=50000.0)
+    btc_df["close"] = 50000.0  # 모든 캔들 동일
+    eth_df = _make_df(10, base_price=3000.0)
+    features = build_features(xrp_df, "LONG", btc_df=btc_df, eth_df=eth_df)
+    assert features["xrp_btc_rs"] == 0.0
+
+def test_feature_cols_has_21_items():
+    from src.ml_features import FEATURE_COLS
+    assert len(FEATURE_COLS) == 21
+
+
 def make_df(n=100):
     """테스트용 최소 DataFrame 생성"""
     np.random.seed(42)
@@ -27,13 +77,19 @@ def test_build_features_returns_series():
     assert isinstance(features, pd.Series)
 
 
+BASE_FEATURE_COLS = [
+    "rsi", "macd_hist", "bb_pct", "ema_align",
+    "stoch_k", "stoch_d", "atr_pct", "vol_ratio",
+    "ret_1", "ret_3", "ret_5", "signal_strength", "side",
+]
+
 def test_build_features_has_all_cols():
     from src.indicators import Indicators
     df = make_df(100)
     ind = Indicators(df)
     df_ind = ind.calculate_all()
     features = build_features(df_ind, signal="LONG")
-    for col in FEATURE_COLS:
+    for col in BASE_FEATURE_COLS:
         assert col in features.index, f"피처 누락: {col}"